You Only Look At Face
This is a demo about face detection.
No paper.
No sota model.
I provide two models, TinyYOLAF(anchor-based) and CenterYOLAF(anchor-free).
They are both fast enough (50-60 FPS on GTX-1060-mobile-3G GPU) and effective.
The AP on widerface val dataset:
| size | Easy | Medium | Hard | |
| TinyYOLAF | 640 | 0.784 | 0.827 | 0.771 |
| CenterYOLAF | 640 | 0.889 | 0.850 | 0.720 |
TinyYOLAF is very simple. Its backbone network is darknet_tiny which is designed by myself.
Since it is an anchor-based method, I design some anchor boxes with kmeans used in YOLOv3. You can open data/config.py to check them.
CenterYOLAF is also very simple. Following CenterFace and CenterNet, I use ResNet-18 as backbone and several deconv to get a heatmap.
However, there are some differences:
-
In CenterNet, it copies the codes from CornerNet to generate a radius for Gauss Kernel who will create a groudtruth heatmap. But, I can't understand why this method is suitable for center. So I apply a different method that I use weight and height of a bounding box to calculate sigma_w and sigma_h. For more details, you can open
tools.pyto see. -
In CenterNet, it uses L1 to learn offset while I use Sigmoid and BCELoss as the offset is between 0 and 1. Just like YOLOv3.
I only download widerface dataset, and evaluate my model on Val dataset.
python train.py -v TinyYOLAF --cuda -hr --num_workers 8
python train.py -v CenterYOLAF --cuda -hr --num_workers 8python widerface_val.py -v TinyYOLAF --trained_model [path_to_model]
python widerface_val.py -v CenterYOLAF --trained_model [path_to_model]python demo.py -v [select a model] --cuda --trained_model [path_to_model] --mode [camera/image/video]CenterYOLAF:
